fast-agent

fast-agent model llamacpp

Any fast-agent setup or program can be used with any ACP client - the simplest way is to use fast-agent-acp:

# Run fast-agent inside Toad
toad acp "fast-agent-acp -x --model sonnet"

fast-agent enables you to create and interact with sophisticated multimodal Agents and Workflows in minutes. It is the first framework with complete, end-to-end tested MCP Feature support including Sampling and Elicitations.

fast-agent is CLI-first, with an optional prompt_toolkit-powered interactive terminal prompt (TUI-style input, completions, and in-terminal menus); responses can stream live to the terminal via rich without relying on full-screen curses UIs or external GUI overlays.

The simple declarative syntax lets you concentrate on composing your Prompts and MCP Servers to build effective agents.

Model support is comprehensive with native support for Anthropic, OpenAI and Google providers as well as Azure, Ollama, Deepseek and dozens of others via TensorZero. Structured Outputs, PDF and Vision support is simple to use and well tested. Passthrough and Playback LLMs enable rapid development and test of Python glue-code for your applications.

Recent features include:

• Agent Skills (SKILL.md)
• MCP-UI Support |
• OpenAI Apps SDK (Skybridge)
• Shell Mode
• Advanced MCP Transport Diagnsotics
• MCP Elicitations

fast-agent is the only tool that allows you to inspect Streamable HTTP Transport usage - a critical feature for ensuring reliable, compliant deployments. OAuth is supported with KeyRing storage for secrets. Use the fast-agent auth command to manage.

[!IMPORTANT]

Documentation is included as a submodule. When cloning, use --recurse-submodules to get everything:

git clone --recurse-submodules https://github.com/evalstate/fast-agent.git

Or if you've already cloned:

git submodule update --init --recursive

The documentation source is also available at: https://github.com/evalstate/fast-agent-docs

Agent Application Development

Prompts and configurations that define your Agent Applications are stored in simple files, with minimal boilerplate, enabling simple management and version control.

Chat with individual Agents and Components before, during and after workflow execution to tune and diagnose your application. Agents can request human input to get additional context for task completion.

Simple model selection makes testing Model <-> MCP Server interaction painless. You can read more about the motivation behind this project here

Get started:

Start by installing the uv package manager for Python. Then:

uv pip install fast-agent-mcp          # install fast-agent!
fast-agent go                          # start an interactive session
fast-agent go --url https://hf.co/mcp  # with a remote MCP
fast-agent go --model=generic.qwen2.5  # use ollama qwen 2.5
fast-agent go --pack analyst --model haiku  # install/reuse a card pack and launch it
fast-agent scaffold                    # create an example agent and config files
uv run agent.py                        # run your first agent
uv run agent.py --model='o3-mini?reasoning=low'    # specify a model
uv run agent.py --transport http --port 8001  # expose as MCP server (server mode implied)
fast-agent quickstart workflow  # create "building effective agents" examples

--server remains available for backward compatibility but is deprecated; --transport now automatically switches an agent into server mode.

For packaged starter agents, use fast-agent go --pack <name> --model <model>. This installs the pack into the selected fast-agent environment if needed, then starts go normally. --model is a fallback for cards without an explicit model setting; a model declared directly in an AgentCard still wins.

Other quickstart examples include a Researcher Agent (with Evaluator-Optimizer workflow) and Data Analysis Agent (similar to the ChatGPT experience), demonstrating MCP Roots support.

[!TIP] Windows Users - there are a couple of configuration changes needed for the Filesystem and Docker MCP Servers - necessary changes are detailed within the configuration files.

Basic Agents

Defining an agent is as simple as:

@fast.agent(
  instruction="Given an object, respond only with an estimate of its size."
)

We can then send messages to the Agent:

async with fast.run() as agent:
  moon_size = await agent("the moon")
  print(moon_size)

Or start an interactive chat with the Agent:

async with fast.run() as agent:
  await agent.interactive()

Here is the complete sizer.py Agent application, with boilerplate code:

import asyncio
from fast_agent import FastAgent

# Create the application
fast = FastAgent("Agent Example")

@fast.agent(
  instruction="Given an object, respond only with an estimate of its size."
)
async def main():
  async with fast.run() as agent:
    await agent.interactive()

if __name__ == "__main__":
    asyncio.run(main())

The Agent can then be run with uv run sizer.py.

Specify a model with the --model switch - for example uv run sizer.py --model sonnet.

Model strings also accept query overrides. For example:

• uv run sizer.py --model "gpt-5?reasoning=low"
• uv run sizer.py --model "claude-sonnet-4-6?web_search=on"
• uv run sizer.py --model "claude-sonnet-4-5?context=1m"

For Anthropic models, ?context=1m is only needed for earlier Sonnet 4 / Sonnet 4.5 models that still require the explicit 1M context opt-in. Claude Sonnet 4.6 and Claude Opus 4.6 already use their long context window by default, so ?context=1m is accepted for backward compatibility but is unnecessary there.

Combining Agents and using MCP Servers

To generate examples use fast-agent quickstart workflow. This example can be run with uv run workflow/chaining.py. fast-agent looks for configuration files in the current directory before checking parent directories recursively.

Agents can be chained to build a workflow, using MCP Servers defined in the fastagent.config.yaml file:

@fast.agent(
    "url_fetcher",
    "Given a URL, provide a complete and comprehensive summary",
    servers=["fetch"], # Name of an MCP Server defined in fastagent.config.yaml
)
@fast.agent(
    "social_media",
    """
    Write a 280 character social media post for any given text.
    Respond only with the post, never use hashtags.
    """,
)
@fast.chain(
    name="post_writer",
    sequence=["url_fetcher", "social_media"],
)
async def main():
    async with fast.run() as agent:
        # using chain workflow
        await agent.post_writer("http://llmindset.co.uk")

All Agents and Workflows respond to .send("message") or .prompt() to begin a chat session.

Saved as social.py we can now run this workflow from the command line with:

uv run workflow/chaining.py --agent post_writer --message "<url>"

Add the --quiet switch to disable progress and message display and return only the final response - useful for simple automations.

MAKER

MAKER (“Massivel